code: def forward(self, input_):
      self._check_input_dim(input_)
      n = input_.size()[0]
  
      if self.training:
          mean = torch.mean(input_, dim=0)
  
          sum = torch.sum((input_ - mean.expand_as(input_)) ** 2, dim=0)
          if sum == 0 and self.eps == 0:
              invstd = 0.0
          else:
              invstd = 1.0 / torch.sqrt(sum / n + self.eps)
          unbiased_var = sum / (n - 1)
  
          self.r = torch.clamp(
              torch.sqrt(unbiased_var).data
              / torch.sqrt(self.running_var),
              1.0 / self.rmax,
              self.rmax,
          )
          self.d = torch.clamp(
              (mean.data - self.running_mean)
              / torch.sqrt(self.running_var),
              -self.dmax,
              self.dmax,
          )
  
          r = self.r.expand_as(input_)
          d = self.d.expand_as(input_)
  
          input_normalized = (
              input_ - mean.expand_as(input_)
          ) * invstd.expand_as(input_)
  
          input_normalized = input_normalized * r + d
  
          self.running_mean += self.momentum * (
              mean.data - self.running_mean
          )
          self.running_var += self.momentum * (
              unbiased_var.data - self.running_var
          )
  
          if not self.affine:
              return input_normalized
  
          output = input_normalized * self.weight.expand_as(input_)
          output += self.bias.unsqueeze(0).expand_as(input_)
  
          return output
  
      else:
          mean = self.running_mean.expand_as(input_)
          invstd = 1.0 / torch.sqrt(
              self.running_var.expand_as(input_) + self.eps
          )
  
          input_normalized = (
              input_ - mean.expand_as(input_)
          ) * invstd.expand_as(input_)
  
          if not self.affine:
              return input_normalized
  
          output = input_normalized * self.weight.expand_as(input_)
          output += self.bias.unsqueeze(0).expand_as(input_)
  
          return output
  
file_path: pywick/pywick/functions/batchrenorm.py
imports: import torch
  from torch.nn.modules import Module
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 4 for calling the BatchReNorm1d obj's underlying member method code with example usage: obj.forward(input_) and returns: output, output, input_normalized, input_normalized